Introduction: Coronavirus disease 2019 (COVID-19) is a serious illness that can affect multiple organs including the lungs. The COVID-mortality risk is attributed to the quick transmission of the virus, the severity of disease, and preclinical risk factors, such as the presence of comorbidities. High-resolution computed tomography (HRCT) can predict disease severity in COVID-19 patients.

Methodology: This was a retrospective cohort study in which data were obtained from COVID centers at tertiary care hospitals in Azad Jammu and Kashmir. Details of clinical characteristics and HRCT findings along with details of smoking and comorbid history were obtained.

Results: Fever at hospital admission, HRCT findings, and having a partner predicted disease severity showed a significant p-value of <0.05. Old age and living in a combined household were associated with severe outcomes (p<0.05). Symptoms of shortness of breath (SOB) on hospital admission could predict the need for ICU admission in COVID-19 patients.

Conclusion: HRCT has a good predictive value for disease severity in patients with COVID-19, and old age is a risk factor. Although, limited associations were established in the analysis, in this study hyperlipidemia and hypertension significantly affected the course of disease. Further studies should be done to explore the relationship.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10893976PMC
http://dx.doi.org/10.7759/cureus.52937DOI Listing

Publication Analysis

Top Keywords

hrct findings
12
disease severity
12
disease
5
exploring relationship
4
relationship comorbidities
4
comorbidities smoking
4
smoking status
4
hrct
4
status hrct
4
findings covid-19
4

Similar Publications

Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF.

View Article and Find Full Text PDF

Background: This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).

Methods: Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions.

View Article and Find Full Text PDF

Background: It is documented that COVID-19 survivors have prolonged morbidity and functional impairment for many years. Data regarding post-COVID-19 lung functions is lacking from the Indian population. We aim to evaluate the lung functions in such patients after 3-6 months of hospital discharge.

View Article and Find Full Text PDF

Background: This study aims to compare Lung Ultrasound (LUS) findings with High-Resolution Computerized Tomography (HRCT) and Pulmonary Function Tests (PFTs) to detect the severity of lung involvement in patients with Usual Interstitial Pneumonia (UIP) and Non-Specific Interstitial Pneumonia (NSIP).

Methods: A cross-sectional study was conducted on 35 UIP and 30 NSIP patients at a referral hospital. All patients underwent LUS, HRCT, and PFT.

View Article and Find Full Text PDF

Artificial Intelligence Unveils the Unseen: Mapping Novel Lung Patterns in Bronchiectasis via Texture Analysis.

Diagnostics (Basel)

December 2024

Department of Respiratory Medicine, JSS Medical College, JSS Academy of Higher Education & Research (JSS AHER), Mysore 570004, Karnataka, India.

Thin-section CT (TSCT) is currently the most sensitive imaging modality for detecting bronchiectasis. However, conventional TSCT or HRCT may overlook subtle lung involvement such as alveolar and interstitial changes. Artificial Intelligence (AI)-based analysis offers the potential to identify novel information on lung parenchymal involvement that is not easily detectable with traditional imaging techniques.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!